# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
垃圾桶模型
"""
import tensorflow as tf
import json
import colorsys
import socket
from timeit import default_timer as timer

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model
import time
graph = tf.get_default_graph()
def IsOpen(ip, port):
    """
    检测端口是否打开
    :param ip:
    :param port:
    :return:
    """
    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    try:
        s.connect((ip, int(port)))
        s.shutdown(2)
        return True
    except:
        return False

#11,12 ,  18,23  , 26,37,  39,58  , 55,102 , 94,148 , 121,240 , 198,290 , 314,351
#'model_data/yolo2_anchorss.txt',
class YOLO_3(object):
    _defaults = {
        "model_path": 'model_data/njbin614.h5',
        "anchors_path": 'model_data/njbin68_anchors.txt',
        "classes_path": 'model_data/njbin68_classes.txt',
        "score" : 0.3,
        "iou" : 0.4,
        "model_image_size" : (416, 416),
        "gpu_num" : 1,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults) # set up default values
        self.__dict__.update(kwargs) # and update with user overrides
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        config = tf.ConfigProto()

        #config.gpu_options.per_process_gpu_memory_fraction = 0.55  # 设定显存的利用率
        #K.set_session(tf.Session(config=config))
        self.sess = K.get_session()

        self.boxes, self.scores, self.classes = self.generate()

    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    def _get_anchors(self):
        anchors_path = os.path.expanduser(self.anchors_path)
        with open(anchors_path) as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(',')]
        return np.array(anchors).reshape(-1, 2)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                'Mismatch between model and given anchor and class sizes'

        print('{} model, anchors, and classes loaded.'.format(model_path))

        # Generate colors for drawing bounding boxes.
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(
            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                self.colors))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if self.gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                len(self.class_names), self.input_image_shape,
                score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes

    def detect_image_3(self, image):
        with graph.as_default():
            start = timer()

            if self.model_image_size != (None, None):
                assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
                assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
                boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
            else:
                new_image_size = (image.width - (image.width % 32),
                                  image.height - (image.height % 32))
                boxed_image = letterbox_image(image, new_image_size)
            image_data = np.array(boxed_image, dtype='float32')
            print("predicting..........................................................")
            #print(image_data.shape)
            image_data /= 255.
            image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

            out_boxes, out_scores, out_classes = self.sess.run(
                [self.boxes, self.scores, self.classes],
                feed_dict={
                    self.yolo_model.input: image_data,
                    self.input_image_shape: [image.size[1], image.size[0]],
                    K.learning_phase(): 0
                })

            #print('Found {} boxes for {}'.format(len(out_boxes), 'img'))

            font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                                      size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
            thickness = (image.size[0] + image.size[1]) // 300
            result_all = ""
            for i, c in reversed(list(enumerate(out_classes))):
                predicted_class = self.class_names[c]
                #print(type(predicted_class))
                box = out_boxes[i]
                score = out_scores[i]

                label = '{}{:.2f}'.format(predicted_class, score)
                draw = ImageDraw.Draw(image)
                label_size = draw.textsize(label, font)

                top, left, bottom, right = box

                top = max(0, np.floor(top + 0.5).astype('int32'))
                left = max(0, np.floor(left + 0.5).astype('int32'))
                bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
                right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
                #print(label, (left, top), (right, bottom))
                label_str_std = str(label).rjust(8, '0')
                top_str_std = str(top).rjust(4, '0')
                left_str_std = str(left).rjust(4, '0')
                bottom_str_std = str(bottom).rjust(4, '0')
                right_str_std = str(right).rjust(4, '0')
                image_width_pixel = str(image.size[0]).rjust(4, '0')
                image_hight_pixel = str(image.size[1]).rjust(4, '0')
                result = label_str_std + top_str_std + left_str_std + bottom_str_std + right_str_std + image_width_pixel + image_hight_pixel
                result_all += result
                # print(result_all)
                # print(result)

            return result_all

    def close_session(self):
        self.sess.close()



